/*! This file is auto-generated */ .wp-block-button__link{color:#fff;background-color:#32373c;border-radius:9999px;box-shadow:none;text-decoration:none;padding:calc(.667em + 2px) calc(1.333em + 2px);font-size:1.125em}.wp-block-file__button{background:#32373c;color:#fff;text-decoration:none} Problem 8 A group of \(n\) customers moves... [FREE SOLUTION] | 91Ó°ÊÓ

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A group of \(n\) customers moves around among two servers. Upon completion of service, the served customer then joins the queue (or enters service if the server is free) at the other server. All service times are exponential with rate \(\mu .\) Find the proportion of time that there are \(j\) customers at server \(1, j=0, \ldots, n\).

Short Answer

Expert verified
The proportion of time that there are \(j\) customers at server \(1\) for \(j = 0, 1, \ldots, n\) is given by: \[P_j = \begin{cases} \frac{2}{n+2}, &\text{if } j \text{ is even} \\ \frac{2}{n+2} \left(1 + \frac{n}{2}\right), &\text{if } j \text{ is odd} \end{cases}\]

Step by step solution

01

Understand the queueing model and the exponential distribution

In this problem, there are two servers, and \(n\) customers move back and forth between the servers. After being served by a server, a customer either enters service at the other server or joins the queue if the other server is busy. We are also told that service times at both servers follow an exponential distribution with parameter \(\mu\). The exponential distribution is a continuous probability distribution that describes the time between events in a Poisson point process. In this case, it describes the time a server spends serving a customer. The exponential distribution can be represented as \(f(t) = \mu e^{-\mu t}\), where \(t\) is the time and \(\mu\) is the rate parameter.
02

Set up the balance equation

To find the proportion of time that there are \(j\) customers at server \(1\), we can compute the stationary distribution of customers in the system. The stationary distribution is the probability distribution of the system’s state when time goes to infinity. Based on the given scenario, the balance equations can be defined as: For \(j=0\): \[P_0 \mu + P_{n-1} \mu = P_1 \mu\] For \(1 \leq j \leq n-1\): \[P_{j-1} \mu + P_{j+1} \mu = P_j (\mu + \mu)\] For \(j=1\), specifically: \[P_0 \mu + P_2 \mu = P_1 2\mu\]
03

Solve the balance equation

Solve the balance equations to find the probability, \(P_j\), of having \(j\) customers at server \(1\). From the equation for \(j=0\), we have: \[P_1 = P_0 + \frac{P_{n-1}}{2}\] From the equation for \(1 \leq j \leq n-1\), we observe that for any \(j\), \(P_{j+1} = P_{j-1}\). This means that for every non-negative integer \(k \leq n-2\), we have \[P_{2k+1} = P_{2k-1}\] Based on this, we can compute \(P_j\) for any \(j\) from \(P_0\): \[P_j = \begin{cases} P_0, &\text{if } j \text{ is even} \\ P_0 + \frac{P_{n-1}}{2}, &\text{if } j \text{ is odd} \end{cases}\] Notice that since \(P_0 + P_1 + \ldots + P_n = 1\) and \(P_{2k+1} = P_{2k-1}\) for every \(k\), \[P_0 \left(1 + \frac12 + \frac12 + \ldots + \frac12 \right) + \frac{P_{n-1}}{2} \left( 1 + 1 + \ldots + 1 \right) = 1\] \[P_0 \left( 1 + \frac{n}{2} \right) + \frac{P_{n-1}}{2} \left( \lfloor \frac{n+1}{2}\rfloor \right) = 1\] Now, we can compute \(P_0\) and \(P_{n-1}\): \[P_{0} = \frac{2}{n+2}\] \[P_{n-1} = 2P_0\] Finally, we can compute \(P_j\) as: \[P_j = \begin{cases} \frac{2}{n+2}, &\text{if } j \text{ is even} \\ \frac{2}{n+2} \left(1 + \frac{n}{2}\right), &\text{if } j \text{ is odd} \end{cases}\] These probabilities represent the proportion of time that there are \(j\) customers at server \(1\) for \(j = 0, 1, \ldots, n\).

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Most popular questions from this chapter

For the \(M / G / 1\) queue, let \(X_{n}\) denote the number in the system left behind by the \(n\) th departure. (a) If $$ X_{n+1}=\left\\{\begin{array}{ll} X_{n}-1+Y_{n}, & \text { if } X_{n} \geqslant 1 \\ Y_{n}, & \text { if } X_{n}=0 \end{array}\right. $$ what does \(Y_{n}\) represent? (b) Rewrite the preceding as $$ X_{n+1}=X_{n}-1+Y_{n}+\delta_{n} $$ where $$ \delta_{n}=\left\\{\begin{array}{ll} 1, & \text { if } X_{n}=0 \\ 0, & \text { if } X_{n} \geqslant 1 \end{array}\right. $$ Take expectations and let \(n \rightarrow \infty\) in Equation (8.64) to obtain $$ E\left[\delta_{\infty}\right]=1-\lambda E[S] $$ (c) Square both sides of Equation (8.64), take expectations, and then let \(n \rightarrow\) \(\infty\) to obtain $$ E\left[X_{\infty}\right]=\frac{\lambda^{2} E\left[S^{2}\right]}{2(1-\lambda E[S])}+\lambda E[S] $$ (d) Argue that \(E\left[X_{\infty}\right]\), the average number as seen by a departure, is equal to \(L\).

The economy alternates between good and bad periods. During good times customers arrive at a certain single-server queueing system in accordance with a Poisson process with rate \(\lambda_{1}\), and during bad times they arrive in accordance with a Poisson process with rate \(\lambda_{2}\). A good time period lasts for an exponentially distributed time with rate \(\alpha_{1}\), and a bad time period lasts for an exponential time with rate \(\alpha_{2}\). An arriving customer will only enter the queueing system if the server is free; an arrival finding the server busy goes away. All service times are exponential with rate \(\mu\). (a) Define states so as to be able to analyze this system. (b) Give a set of linear equations whose solution will yield the long run proportion of time the system is in each state. In terms of the solutions of the equations in part (b), (c) what proportion of time is the system empty? (d) what is the average rate at which customers enter the system?

It follows from Exercise 4 that if, in the \(M / M / 1\) model, \(W_{Q}^{*}\) is the amount of time that a customer spends waiting in queue, then $$ W_{Q}^{*}=\left\\{\begin{array}{ll} 0, & \text { with probability } 1-\lambda / \mu \\ \operatorname{Exp}(\mu-\lambda), & \text { with probability } \lambda / \mu \end{array}\right. $$ where \(\operatorname{Exp}(\mu-\lambda)\) is an exponential random variable with rate \(\mu-\lambda\). Using this, find \(\operatorname{Var}\left(W_{Q}^{*}\right)\)

A group of \(m\) customers frequents a single-server station in the following manner. When a customer arrives, he or she either enters service if the server is free or joins the queue otherwise. Upon completing service the customer departs the system, but then returns after an exponential time with rate \(\theta\). All service times are exponentially distributed with rate \(\mu\). (a) Define states and set up the balance equations. In terms of the solution of the balance equations, find (b) the average rate at which customers enter the station. (c) the average time that a customer spends in the station per visit.

Consider a \(M / G / 1\) system with \(\lambda E[S]<1\). (a) Suppose that service is about to begin at a moment when there are \(n\) customers in the system. (i) Argue that the additional time until there are only \(n-1\) customers in the system has the same distribution as a busy period. (ii) What is the expected additional time until the system is empty? (b) Suppose that the work in the system at some moment is \(A\). We are interested in the expected additional time until the system is empty - call it \(E[T]\). Let \(N\) denote the number of arrivals during the first \(A\) units of time. (i) Compute \(E[T \mid N]\). (ii) Compute \(E[T]\).

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